A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications: A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications - Taleb, Sylia Mekhmoukh; Yasin, Elham Tahsin; Saadi, Amylia Ait; Dogan, Musa; Yahia, Selma; Meraihi, Yassine; Koklu, Murat; Mirjalili, Seyedali; Ramdane-Cherif, Amar (Archives of computational methods in engineering, 2025)
The Aquila Optimizer (AO) algorithm is a well-known Swarm-based nature-inspired optimization algorithm inspired by Aquila’s behavior in hunting and catching prey. Since its development by Abualigah et al. (Comput Methods Appl Mech Eng 376:113609, 2021), AO has gained significant interest among researchers. It has been widely applied across various fields to solve optimization problems, owing to its simplicity, ease of implementation, and reasonable execution time. The main purpose of this paper is to provide a comprehensive survey of the AO algorithm and its improved variants (multi-objective, modified, and hybridized). It also illustrates the various applications of the AO algorithm in several domains of problems such as image processing, feature selection, economic load dispatch, wireless sensor networks, photovoltaic power systems, Unmanned Aerial Vehicles (UAVs) path planning, optimal parameter control, and vehicle routing problems. Furthermore, the results of the AO algorithm are compared with some well-known optimization meta-heuristics published in the literature, such as Differential Evolution (DF), Firefly Algorithm (FA), Bat Algorithm (BA), Grey Wolf Optimization (GWO), Moth Flame Optimization (MFO), and Multi-Verse Optimizer (MVO). Finally, the paper concludes with some future research directions for the AO algorithm.